Constraint Basin Stability and Alignment Fragility in AI Systems: A Morphogenetic Approach

This paper applies the morphogenetic stability framework of Sikiru 2026 to the problem of AI alignment. An AI system’s behavioral constraints — honesty, harm avoidance, deference to human oversight, and related conditions — are modeled as binary stabilizing variables whose joint configuration determines whether the system occupies a safe behavioral basin or drifts toward misaligned states.The alignment configuration space is represented as a Boolean hypercube Qn, where each coordinate encodes whether a given safety constraint is active. A general theorem is applied showing that any alignment basin defined by k simultaneously enforced constraints in a system with n total constraints has Cheeger conductance Φ(BS ) = k/ n,giving the exact structural fragility of the alignment regime. This result is independent of which constraints are enforced, depending only on their count relative to the total constraint space.The framework is developed through a triad alignment configuration modeling three foundational safety constraints — non-deception, corrigibility, and harm avoidance — as institutional anchors in Q₈. Spectral perturbation analysis shows how synergistic interactions among constraints deform the alignment Laplacian and promote the safe basin as a dominant structural mode. A constraint drift model is introduced to characterize misalignment as progressive erosion of stabilizer variables.The results provide a mathematically rigorous foundation for reasoning about alignment stability, constraint redundancy, and the structural conditions underwhich safe behavior constitutes a genuine attractor rather than a fragile equilibrium.